We derive novel approximate closed-form expressions for the nonlinear coupling coefficients appearing in the Manakov equations for multimode fibers for space-division multiplexing in the two regimes of strong and weak coupling. The expressions depend only on few fiber design parameters. In particular, the Manakov coefficients are shown to be simple rational numbers which depend solely on the number of guided modes. The overall nonlinearity coefficients are found to decrease with increasing core radius and to stay nearly constant with increasing refractive index difference between core and cladding. Validation is performed through a numerical approach. The consequences of the findings onto fiber design are discussed in terms of achievable data rates. The analysis is mainly focused on the trenchless parabolic graded-index profile, but considerations on the use of realistic trenches and non-parabolic indices, and on the step-index profile are given.

Reliable communication over bandlimited and nonlinear channels usually requires equalization to simplify receiver processing. Equalizers that perform joint detection and decoding (JDD) achieve the highest information rates but are often too complex to implement. To address this challenge, model-based neural network (NN) equalizers that perform successive interference cancellation (SIC) are shown to approach JDD information rates for bandlimited channels with a memoryless nonlinearity and additive white Gaussian noise. The NNs are chosen to have a periodically time-varying and recurrent structure that imitates the forward-backward algorithm (FBA) in every SIC stage. Simulations for short-haul fiber-optic links with square-law detection show that NN-SIC nearly doubles current spectral efficiencies, and bipolar or complex-valued modulations achieve energy gains of up to 3 dB compared to state-of-the-art intensity modulation. Moreover, NN-SIC is considerably less complex than equalizers that perform JDD, mismatched FBA processing, and Gibbs sampling.

Neural networks (NNs) inspired by the forward-backward algorithm (FBA) are used as equalizers for bandlimited channels with a memoryless nonlinearity. The NN-equalizers are combined with successive interference cancellation (SIC) to approach the information rates of joint detection and decoding (JDD) with considerably less complexity than JDD and other existing equalizers. Simulations for short-haul optical fiber links with square-law detection illustrate the gains.

The maximum information rates for bandlimited channels with direct detection are achieved with joint detection and decoding (JDD), but JDD is often too complex to implement. Two receiver structures are studied to reduce complexity: separate detection and decoding (SDD) and successive interference cancellation (SIC). For bipolar modulation, frequency-domain raised-cosine pulse shaping, and fiber-optic channels with chromatic dispersion, SIC achieves rates close to those of JDD, thereby attaining significant energy gains over SDD and intensity modulation. Gibbs sampling further reduces the detector complexity and achieves rates close to those of the forward-backward algorithm at low to intermediate signal-to-noise ratio (SNR) but stalls at high SNR. Simulations with polar codes, higher-order modulation, and multi-level coding confirm the predicted gains.

Automotive Power Line Communication (PLC) is a promising technology that enables data transmission without the need of dedicated, heavy data communication lines. This way, it allows for reducing the weight and wiring complexity of a vehicle. This paper addresses the modelling of an in-vehicle PLC channel and gives a additional understanding of the impact single system components have on the specific automotive PLC transmission characteristic. It is shown that an automotive PLC channel is mainly characterized by the car battery, number of branch (tap) lines and the loads introduced by the various electrical control units. The heoretical results are compared with measurement data in order to validate the simulation model.

The mutual information between a complex-valued channel input and its complex-valued output is decomposed into four parts based on polar coordinates: an amplitude term, a phase term, and two mixed terms. Numerical results for the additive white Gaussian noise (AWGN) channel with various inputs show that, at high signal-to-noise ratio (SNR), the amplitude and phase terms dominate the mixed terms. For the AWGN channel with a Gaussian input, analytical expressions are derived for high SNR. The decomposition method is applied to partially coherent channels and a property of such channels called “spectral loss” is developed. Spectral loss occurs in nonlinear fiber-optic channels and it may be one effect that needs to be taken into account to explain the behavior of the capacity of nonlinear fiber-optic channels.

This paper presents first results of the European research project MOON. It is organized as follows. As a starting point, basic concepts of all-optical networks which are relevant for management are described. The results are compared with the Synchronous Digital Hierarchy (SDH) which is the state-of-the-art transport technique. Then, the general architecture for a TMN conforming management of all-optical networks is illustrated. As a main point of the paper, the impact of transparency limitations on transmission, OAM (operation, administration, maintenance), and management is discussed. A functional architecture will be presented which describes the network in a formalized way. Based on this description, different approaches for the transmission of OAM overhead are addressed. The information model of MOON is described and compared to the SDH model. Finally, concepts for the implementation of the management system are presented.